Overview

Dataset statistics

Number of variables17
Number of observations52924
Missing cells400
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.9 MiB
Average record size in memory136.0 B

Variable types

Text3
DateTime1
Categorical7
Numeric6

Reproduction

Analysis started2024-02-25 22:59:50.886098
Analysis finished2024-02-25 22:59:59.139859
Duration8.25 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

Distinct1468
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size413.6 KiB
2024-02-26T07:59:59.461000image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters476316
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique66 ?
Unique (%)0.1%

Sample

1st rowUSER_1358
2nd rowUSER_1358
3rd rowUSER_1358
4th rowUSER_1358
5th rowUSER_1358
ValueCountFrequency (%)
user_0118 695
 
1.3%
user_0736 587
 
1.1%
user_0563 575
 
1.1%
user_1355 572
 
1.1%
user_0643 523
 
1.0%
user_0202 366
 
0.7%
user_0667 315
 
0.6%
user_1358 297
 
0.6%
user_0572 290
 
0.5%
user_0200 261
 
0.5%
Other values (1458) 48443
91.5%
2024-02-26T07:59:59.873897image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
U 52924
11.1%
S 52924
11.1%
E 52924
11.1%
R 52924
11.1%
_ 52924
11.1%
0 52729
11.1%
1 33865
7.1%
3 18402
 
3.9%
2 18079
 
3.8%
4 16238
 
3.4%
Other values (5) 72383
15.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 211696
44.4%
Decimal Number 211696
44.4%
Connector Punctuation 52924
 
11.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 52729
24.9%
1 33865
16.0%
3 18402
 
8.7%
2 18079
 
8.5%
4 16238
 
7.7%
5 15809
 
7.5%
6 15082
 
7.1%
8 14715
 
7.0%
7 13637
 
6.4%
9 13140
 
6.2%
Uppercase Letter
ValueCountFrequency (%)
U 52924
25.0%
S 52924
25.0%
E 52924
25.0%
R 52924
25.0%
Connector Punctuation
ValueCountFrequency (%)
_ 52924
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 264620
55.6%
Latin 211696
44.4%

Most frequent character per script

Common
ValueCountFrequency (%)
_ 52924
20.0%
0 52729
19.9%
1 33865
12.8%
3 18402
 
7.0%
2 18079
 
6.8%
4 16238
 
6.1%
5 15809
 
6.0%
6 15082
 
5.7%
8 14715
 
5.6%
7 13637
 
5.2%
Latin
ValueCountFrequency (%)
U 52924
25.0%
S 52924
25.0%
E 52924
25.0%
R 52924
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 476316
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U 52924
11.1%
S 52924
11.1%
E 52924
11.1%
R 52924
11.1%
_ 52924
11.1%
0 52729
11.1%
1 33865
7.1%
3 18402
 
3.9%
2 18079
 
3.8%
4 16238
 
3.4%
Other values (5) 72383
15.2%
Distinct25061
Distinct (%)47.4%
Missing0
Missing (%)0.0%
Memory size413.6 KiB
2024-02-26T08:00:00.130212image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length17
Median length17
Mean length16.597895
Min length16

Characters and Unicode

Total characters878427
Distinct characters20
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14637 ?
Unique (%)27.7%

Sample

1st rowTransaction_0000
2nd rowTransaction_0001
3rd rowTransaction_0002
4th rowTransaction_0003
5th rowTransaction_0003
ValueCountFrequency (%)
transaction_12261 35
 
0.1%
transaction_4716 30
 
0.1%
transaction_19047 29
 
0.1%
transaction_13487 28
 
0.1%
transaction_16759 27
 
0.1%
transaction_6767 26
 
< 0.1%
transaction_19791 26
 
< 0.1%
transaction_5941 24
 
< 0.1%
transaction_17903 23
 
< 0.1%
transaction_13065 23
 
< 0.1%
Other values (25051) 52653
99.5%
2024-02-26T08:00:00.616910image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 105848
12.0%
n 105848
12.0%
T 52924
 
6.0%
r 52924
 
6.0%
s 52924
 
6.0%
c 52924
 
6.0%
t 52924
 
6.0%
i 52924
 
6.0%
o 52924
 
6.0%
_ 52924
 
6.0%
Other values (10) 243339
27.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 529240
60.2%
Decimal Number 243339
27.7%
Uppercase Letter 52924
 
6.0%
Connector Punctuation 52924
 
6.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 44323
18.2%
2 31054
12.8%
4 22242
9.1%
3 22174
9.1%
0 21910
9.0%
5 20568
8.5%
6 20543
8.4%
7 20190
8.3%
8 20174
8.3%
9 20161
8.3%
Lowercase Letter
ValueCountFrequency (%)
a 105848
20.0%
n 105848
20.0%
r 52924
10.0%
s 52924
10.0%
c 52924
10.0%
t 52924
10.0%
i 52924
10.0%
o 52924
10.0%
Uppercase Letter
ValueCountFrequency (%)
T 52924
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 52924
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 582164
66.3%
Common 296263
33.7%

Most frequent character per script

Common
ValueCountFrequency (%)
_ 52924
17.9%
1 44323
15.0%
2 31054
10.5%
4 22242
7.5%
3 22174
7.5%
0 21910
7.4%
5 20568
 
6.9%
6 20543
 
6.9%
7 20190
 
6.8%
8 20174
 
6.8%
Latin
ValueCountFrequency (%)
a 105848
18.2%
n 105848
18.2%
T 52924
9.1%
r 52924
9.1%
s 52924
9.1%
c 52924
9.1%
t 52924
9.1%
i 52924
9.1%
o 52924
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 878427
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 105848
12.0%
n 105848
12.0%
T 52924
 
6.0%
r 52924
 
6.0%
s 52924
 
6.0%
c 52924
 
6.0%
t 52924
 
6.0%
i 52924
 
6.0%
o 52924
 
6.0%
_ 52924
 
6.0%
Other values (10) 243339
27.7%
Distinct365
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size413.6 KiB
Minimum2019-01-01 00:00:00
Maximum2019-12-31 00:00:00
2024-02-26T08:00:00.783464image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-26T08:00:01.001879image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct1145
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size413.6 KiB
2024-02-26T08:00:01.311054image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters635088
Distinct characters18
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique90 ?
Unique (%)0.2%

Sample

1st rowProduct_0981
2nd rowProduct_0981
3rd rowProduct_0904
4th rowProduct_0203
5th rowProduct_0848
ValueCountFrequency (%)
product_0981 3511
 
6.6%
product_0983 3328
 
6.3%
product_0976 3230
 
6.1%
product_0984 1361
 
2.6%
product_0989 1089
 
2.1%
product_0985 1065
 
2.0%
product_0992 844
 
1.6%
product_0904 806
 
1.5%
product_0990 599
 
1.1%
product_0880 583
 
1.1%
Other values (1135) 36508
69.0%
2024-02-26T08:00:01.739907image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 62552
9.8%
P 52924
8.3%
r 52924
8.3%
o 52924
8.3%
d 52924
8.3%
u 52924
8.3%
c 52924
8.3%
t 52924
8.3%
_ 52924
8.3%
9 36945
 
5.8%
Other values (8) 112199
17.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 317544
50.0%
Decimal Number 211696
33.3%
Uppercase Letter 52924
 
8.3%
Connector Punctuation 52924
 
8.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 62552
29.5%
9 36945
17.5%
8 23570
 
11.1%
1 17721
 
8.4%
4 13315
 
6.3%
2 13135
 
6.2%
3 12135
 
5.7%
6 11047
 
5.2%
7 10872
 
5.1%
5 10404
 
4.9%
Lowercase Letter
ValueCountFrequency (%)
r 52924
16.7%
o 52924
16.7%
d 52924
16.7%
u 52924
16.7%
c 52924
16.7%
t 52924
16.7%
Uppercase Letter
ValueCountFrequency (%)
P 52924
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 52924
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 370468
58.3%
Common 264620
41.7%

Most frequent character per script

Common
ValueCountFrequency (%)
0 62552
23.6%
_ 52924
20.0%
9 36945
14.0%
8 23570
 
8.9%
1 17721
 
6.7%
4 13315
 
5.0%
2 13135
 
5.0%
3 12135
 
4.6%
6 11047
 
4.2%
7 10872
 
4.1%
Latin
ValueCountFrequency (%)
P 52924
14.3%
r 52924
14.3%
o 52924
14.3%
d 52924
14.3%
u 52924
14.3%
c 52924
14.3%
t 52924
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 635088
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 62552
9.8%
P 52924
8.3%
r 52924
8.3%
o 52924
8.3%
d 52924
8.3%
u 52924
8.3%
c 52924
8.3%
t 52924
8.3%
_ 52924
8.3%
9 36945
 
5.8%
Other values (8) 112199
17.7%
Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size413.6 KiB
Apparel
18126 
Nest-USA
14013 
Office
6513 
Drinkware
3483 
Lifestyle
3092 
Other values (15)
7697 

Length

Max length20
Median length11
Mean length7.3746504
Min length3

Characters and Unicode

Total characters390296
Distinct characters38
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNest-USA
2nd rowNest-USA
3rd rowOffice
4th rowApparel
5th rowBags

Common Values

ValueCountFrequency (%)
Apparel 18126
34.2%
Nest-USA 14013
26.5%
Office 6513
 
12.3%
Drinkware 3483
 
6.6%
Lifestyle 3092
 
5.8%
Nest 2198
 
4.2%
Bags 1882
 
3.6%
Headgear 771
 
1.5%
Notebooks & Journals 749
 
1.4%
Waze 554
 
1.0%
Other values (10) 1543
 
2.9%

Length

2024-02-26T08:00:01.901474image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
apparel 18126
33.2%
nest-usa 14013
25.7%
office 6513
 
11.9%
drinkware 3483
 
6.4%
lifestyle 3092
 
5.7%
nest 2198
 
4.0%
bags 1928
 
3.5%
headgear 771
 
1.4%
notebooks 749
 
1.4%
749
 
1.4%
Other values (13) 3005
 
5.5%

Most occurring characters

ValueCountFrequency (%)
e 54810
14.0%
p 36341
 
9.3%
A 32416
 
8.3%
a 27792
 
7.1%
r 27216
 
7.0%
s 24508
 
6.3%
l 22340
 
5.7%
t 21064
 
5.4%
N 17277
 
4.4%
f 16277
 
4.2%
Other values (28) 110255
28.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 277280
71.0%
Uppercase Letter 96234
 
24.7%
Dash Punctuation 14330
 
3.7%
Space Separator 1703
 
0.4%
Other Punctuation 749
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 54810
19.8%
p 36341
13.1%
a 27792
10.0%
r 27216
9.8%
s 24508
8.8%
l 22340
8.1%
t 21064
 
7.6%
f 16277
 
5.9%
i 13524
 
4.9%
c 7159
 
2.6%
Other values (10) 26249
9.5%
Uppercase Letter
ValueCountFrequency (%)
A 32416
33.7%
N 17277
18.0%
S 14013
14.6%
U 14013
14.6%
O 6513
 
6.8%
D 3483
 
3.6%
L 3092
 
3.2%
B 2285
 
2.4%
H 893
 
0.9%
J 749
 
0.8%
Other values (5) 1500
 
1.6%
Dash Punctuation
ValueCountFrequency (%)
- 14330
100.0%
Space Separator
ValueCountFrequency (%)
1703
100.0%
Other Punctuation
ValueCountFrequency (%)
& 749
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 373514
95.7%
Common 16782
 
4.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 54810
14.7%
p 36341
 
9.7%
A 32416
 
8.7%
a 27792
 
7.4%
r 27216
 
7.3%
s 24508
 
6.6%
l 22340
 
6.0%
t 21064
 
5.6%
N 17277
 
4.6%
f 16277
 
4.4%
Other values (25) 93473
25.0%
Common
ValueCountFrequency (%)
- 14330
85.4%
1703
 
10.1%
& 749
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 390296
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 54810
14.0%
p 36341
 
9.3%
A 32416
 
8.3%
a 27792
 
7.1%
r 27216
 
7.0%
s 24508
 
6.3%
l 22340
 
5.7%
t 21064
 
5.4%
N 17277
 
4.4%
f 16277
 
4.2%
Other values (28) 110255
28.2%

수량
Real number (ℝ)

Distinct151
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4976381
Minimum1
Maximum900
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size413.6 KiB
2024-02-26T08:00:02.057058image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile16
Maximum900
Range899
Interquartile range (IQR)1

Descriptive statistics

Standard deviation20.104711
Coefficient of variation (CV)4.4700597
Kurtosis525.45248
Mean4.4976381
Median Absolute Deviation (MAD)0
Skewness19.034802
Sum238033
Variance404.1994
MonotonicityNot monotonic
2024-02-26T08:00:02.213641image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 35336
66.8%
2 7016
 
13.3%
3 2288
 
4.3%
5 1734
 
3.3%
4 1237
 
2.3%
10 1035
 
2.0%
20 531
 
1.0%
6 435
 
0.8%
15 389
 
0.7%
25 287
 
0.5%
Other values (141) 2636
 
5.0%
ValueCountFrequency (%)
1 35336
66.8%
2 7016
 
13.3%
3 2288
 
4.3%
4 1237
 
2.3%
5 1734
 
3.3%
6 435
 
0.8%
7 180
 
0.3%
8 240
 
0.5%
9 57
 
0.1%
10 1035
 
2.0%
ValueCountFrequency (%)
900 1
 
< 0.1%
825 2
 
< 0.1%
791 1
 
< 0.1%
750 1
 
< 0.1%
600 5
< 0.1%
563 1
 
< 0.1%
523 1
 
< 0.1%
516 1
 
< 0.1%
500 12
< 0.1%
475 3
 
< 0.1%

평균금액
Real number (ℝ)

Distinct546
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.237646
Minimum0.39
Maximum355.74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size413.6 KiB
2024-02-26T08:00:02.382189image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.39
5-th percentile1.99
Q15.7
median16.99
Q3102.13
95-th percentile151.88
Maximum355.74
Range355.35
Interquartile range (IQR)96.43

Descriptive statistics

Standard deviation64.006882
Coefficient of variation (CV)1.2253018
Kurtosis3.3424017
Mean52.237646
Median Absolute Deviation (MAD)14.19
Skewness1.6325798
Sum2764625.2
Variance4096.8809
MonotonicityNot monotonic
2024-02-26T08:00:02.573677image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
119 5127
 
9.7%
149 3824
 
7.2%
79 1937
 
3.7%
13.59 1530
 
2.9%
2.39 1284
 
2.4%
2.99 1226
 
2.3%
16.99 1036
 
2.0%
15.19 984
 
1.9%
1.99 956
 
1.8%
3.99 949
 
1.8%
Other values (536) 34071
64.4%
ValueCountFrequency (%)
0.39 1
 
< 0.1%
0.4 45
 
0.1%
0.41 15
 
< 0.1%
0.5 33
 
0.1%
0.51 27
 
0.1%
0.79 164
0.3%
0.8 14
 
< 0.1%
0.81 21
 
< 0.1%
0.97 1
 
< 0.1%
0.98 16
 
< 0.1%
ValueCountFrequency (%)
355.74 169
0.3%
349 330
0.6%
279 147
0.3%
274.19 3
 
< 0.1%
269 1
 
< 0.1%
256.88 7
 
< 0.1%
254.82 1
 
< 0.1%
250 44
 
0.1%
249 26
 
< 0.1%
205.3 33
 
0.1%

배송료
Real number (ℝ)

Distinct267
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.51763
Minimum0
Maximum521.36
Zeros162
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size413.6 KiB
2024-02-26T08:00:02.768157image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q16
median6
Q36.5
95-th percentile26.43
Maximum521.36
Range521.36
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation19.475613
Coefficient of variation (CV)1.8517111
Kurtosis204.63769
Mean10.51763
Median Absolute Deviation (MAD)0
Skewness11.959739
Sum556635.07
Variance379.29951
MonotonicityNot monotonic
2024-02-26T08:00:02.916759image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 26801
50.6%
6.5 15819
29.9%
12.99 2532
 
4.8%
19.99 1042
 
2.0%
12.48 798
 
1.5%
12.91 454
 
0.9%
8.7 325
 
0.6%
0 162
 
0.3%
18.47 139
 
0.3%
13.38 111
 
0.2%
Other values (257) 4741
 
9.0%
ValueCountFrequency (%)
0 162
 
0.3%
6 26801
50.6%
6.46 14
 
< 0.1%
6.48 29
 
0.1%
6.5 15819
29.9%
6.51 18
 
< 0.1%
8.36 4
 
< 0.1%
8.7 325
 
0.6%
8.91 7
 
< 0.1%
11.1 17
 
< 0.1%
ValueCountFrequency (%)
521.36 1
 
< 0.1%
504 2
 
< 0.1%
492.84 10
 
< 0.1%
422.24 4
 
< 0.1%
354 3
 
< 0.1%
351.64 2
 
< 0.1%
344.78 5
 
< 0.1%
324 29
0.1%
323.47 7
 
< 0.1%
307.47 1
 
< 0.1%

쿠폰상태
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size413.6 KiB
Clicked
26926 
Used
17904 
Not Used
8094 

Length

Max length8
Median length7
Mean length6.138047
Min length4

Characters and Unicode

Total characters324850
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUsed
2nd rowUsed
3rd rowUsed
4th rowNot Used
5th rowUsed

Common Values

ValueCountFrequency (%)
Clicked 26926
50.9%
Used 17904
33.8%
Not Used 8094
 
15.3%

Length

2024-02-26T08:00:03.070349image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-26T08:00:03.189032image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
clicked 26926
44.1%
used 25998
42.6%
not 8094
 
13.3%

Most occurring characters

ValueCountFrequency (%)
e 52924
16.3%
d 52924
16.3%
C 26926
8.3%
l 26926
8.3%
i 26926
8.3%
c 26926
8.3%
k 26926
8.3%
U 25998
8.0%
s 25998
8.0%
N 8094
 
2.5%
Other values (3) 24282
7.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 255738
78.7%
Uppercase Letter 61018
 
18.8%
Space Separator 8094
 
2.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 52924
20.7%
d 52924
20.7%
l 26926
10.5%
i 26926
10.5%
c 26926
10.5%
k 26926
10.5%
s 25998
10.2%
o 8094
 
3.2%
t 8094
 
3.2%
Uppercase Letter
ValueCountFrequency (%)
C 26926
44.1%
U 25998
42.6%
N 8094
 
13.3%
Space Separator
ValueCountFrequency (%)
8094
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 316756
97.5%
Common 8094
 
2.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 52924
16.7%
d 52924
16.7%
C 26926
8.5%
l 26926
8.5%
i 26926
8.5%
c 26926
8.5%
k 26926
8.5%
U 25998
8.2%
s 25998
8.2%
N 8094
 
2.6%
Other values (2) 16188
 
5.1%
Common
ValueCountFrequency (%)
8094
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 324850
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 52924
16.3%
d 52924
16.3%
C 26926
8.3%
l 26926
8.3%
i 26926
8.3%
c 26926
8.3%
k 26926
8.3%
U 25998
8.0%
s 25998
8.0%
N 8094
 
2.5%
Other values (3) 24282
7.5%

성별
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size413.6 KiB
33007 
19917 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters52924
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
33007
62.4%
19917
37.6%

Length

2024-02-26T08:00:03.297741image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-26T08:00:03.378524image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
33007
62.4%
19917
37.6%

Most occurring characters

ValueCountFrequency (%)
33007
62.4%
19917
37.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 52924
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
33007
62.4%
19917
37.6%

Most occurring scripts

ValueCountFrequency (%)
Hangul 52924
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
33007
62.4%
19917
37.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 52924
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
33007
62.4%
19917
37.6%

고객지역
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size413.6 KiB
Chicago
18380 
California
16136 
New York
11173 
New Jersey
4503 
Washington DC
2732 

Length

Max length13
Median length10
Mean length8.6907641
Min length7

Characters and Unicode

Total characters459950
Distinct characters23
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowChicago
2nd rowChicago
3rd rowChicago
4th rowChicago
5th rowChicago

Common Values

ValueCountFrequency (%)
Chicago 18380
34.7%
California 16136
30.5%
New York 11173
21.1%
New Jersey 4503
 
8.5%
Washington DC 2732
 
5.2%

Length

2024-02-26T08:00:03.477261image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-26T08:00:03.588963image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
chicago 18380
25.8%
california 16136
22.6%
new 15676
22.0%
york 11173
15.7%
jersey 4503
 
6.3%
washington 2732
 
3.8%
dc 2732
 
3.8%

Most occurring characters

ValueCountFrequency (%)
i 53384
11.6%
a 53384
11.6%
o 48421
 
10.5%
C 37248
 
8.1%
r 31812
 
6.9%
e 24682
 
5.4%
n 21600
 
4.7%
g 21112
 
4.6%
h 21112
 
4.6%
18408
 
4.0%
Other values (13) 128787
28.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 367478
79.9%
Uppercase Letter 74064
 
16.1%
Space Separator 18408
 
4.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 53384
14.5%
a 53384
14.5%
o 48421
13.2%
r 31812
8.7%
e 24682
6.7%
n 21600
 
5.9%
g 21112
 
5.7%
h 21112
 
5.7%
c 18380
 
5.0%
l 16136
 
4.4%
Other values (6) 57455
15.6%
Uppercase Letter
ValueCountFrequency (%)
C 37248
50.3%
N 15676
21.2%
Y 11173
 
15.1%
J 4503
 
6.1%
W 2732
 
3.7%
D 2732
 
3.7%
Space Separator
ValueCountFrequency (%)
18408
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 441542
96.0%
Common 18408
 
4.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 53384
12.1%
a 53384
12.1%
o 48421
11.0%
C 37248
 
8.4%
r 31812
 
7.2%
e 24682
 
5.6%
n 21600
 
4.9%
g 21112
 
4.8%
h 21112
 
4.8%
c 18380
 
4.2%
Other values (12) 110407
25.0%
Common
ValueCountFrequency (%)
18408
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 459950
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 53384
11.6%
a 53384
11.6%
o 48421
 
10.5%
C 37248
 
8.1%
r 31812
 
6.9%
e 24682
 
5.4%
n 21600
 
4.7%
g 21112
 
4.6%
h 21112
 
4.6%
18408
 
4.0%
Other values (13) 128787
28.0%

가입기간
Real number (ℝ)

Distinct49
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.127995
Minimum2
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size413.6 KiB
2024-02-26T08:00:03.733575image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5
Q115
median27
Q337
95-th percentile47
Maximum50
Range48
Interquartile range (IQR)22

Descriptive statistics

Standard deviation13.478285
Coefficient of variation (CV)0.51585609
Kurtosis-1.1171716
Mean26.127995
Median Absolute Deviation (MAD)11
Skewness-0.06955528
Sum1382798
Variance181.66417
MonotonicityNot monotonic
2024-02-26T08:00:03.887166image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
40 2043
 
3.9%
25 1853
 
3.5%
34 1670
 
3.2%
30 1656
 
3.1%
33 1648
 
3.1%
21 1590
 
3.0%
5 1543
 
2.9%
45 1469
 
2.8%
10 1448
 
2.7%
28 1417
 
2.7%
Other values (39) 36587
69.1%
ValueCountFrequency (%)
2 649
1.2%
3 625
1.2%
4 1055
2.0%
5 1543
2.9%
6 1296
2.4%
7 985
1.9%
8 1202
2.3%
9 626
1.2%
10 1448
2.7%
11 902
1.7%
ValueCountFrequency (%)
50 737
1.4%
49 841
1.6%
48 884
1.7%
47 497
 
0.9%
46 756
1.4%
45 1469
2.8%
44 1223
2.3%
43 889
1.7%
42 755
1.4%
41 985
1.9%

오프라인비용
Real number (ℝ)

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2830.9141
Minimum500
Maximum5000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size413.6 KiB
2024-02-26T08:00:04.046738image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile1000
Q12500
median3000
Q33500
95-th percentile4500
Maximum5000
Range4500
Interquartile range (IQR)1000

Descriptive statistics

Standard deviation936.15425
Coefficient of variation (CV)0.33068973
Kurtosis0.10199859
Mean2830.9141
Median Absolute Deviation (MAD)500
Skewness-0.31680745
Sum1.498233 × 108
Variance876384.77
MonotonicityNot monotonic
2024-02-26T08:00:04.173400image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
3000 13529
25.6%
2500 10333
19.5%
3500 9008
17.0%
2000 6019
11.4%
4000 4882
 
9.2%
1500 2411
 
4.6%
4500 2115
 
4.0%
1000 1840
 
3.5%
500 985
 
1.9%
700 969
 
1.8%
ValueCountFrequency (%)
500 985
 
1.9%
700 969
 
1.8%
1000 1840
 
3.5%
1500 2411
 
4.6%
2000 6019
11.4%
2500 10333
19.5%
3000 13529
25.6%
3500 9008
17.0%
4000 4882
 
9.2%
4500 2115
 
4.0%
ValueCountFrequency (%)
5000 833
 
1.6%
4500 2115
 
4.0%
4000 4882
 
9.2%
3500 9008
17.0%
3000 13529
25.6%
2500 10333
19.5%
2000 6019
11.4%
1500 2411
 
4.6%
1000 1840
 
3.5%
700 969
 
1.8%

온라인비용
Real number (ℝ)

Distinct365
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1893.1091
Minimum320.25
Maximum4556.93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size413.6 KiB
2024-02-26T08:00:04.312029image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum320.25
5-th percentile682.42
Q11252.63
median1837.87
Q32425.35
95-th percentile3396.14
Maximum4556.93
Range4236.68
Interquartile range (IQR)1172.72

Descriptive statistics

Standard deviation807.01409
Coefficient of variation (CV)0.42629032
Kurtosis-0.14764686
Mean1893.1091
Median Absolute Deviation (MAD)587.1
Skewness0.45430588
Sum1.0019091 × 108
Variance651271.74
MonotonicityNot monotonic
2024-02-26T08:00:04.450311image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2819.58 335
 
0.6%
2489.36 311
 
0.6%
1692.8 298
 
0.6%
2155.96 292
 
0.6%
985.28 291
 
0.5%
2563.83 289
 
0.5%
1292.58 278
 
0.5%
663.46 274
 
0.5%
1331.1 269
 
0.5%
1172.96 264
 
0.5%
Other values (355) 50023
94.5%
ValueCountFrequency (%)
320.25 130
0.2%
417.73 185
0.3%
465.4 43
 
0.1%
478.27 131
0.2%
484.9 160
0.3%
515.44 118
0.2%
526.96 96
0.2%
560.02 140
0.3%
620.94 135
0.3%
640.93 56
 
0.1%
ValueCountFrequency (%)
4556.93 141
0.3%
4349.02 124
0.2%
4055.3 189
0.4%
4019.93 61
 
0.1%
3897.2 129
0.2%
3875.08 163
0.3%
3796.85 159
0.3%
3784.07 133
0.3%
3749.46 35
 
0.1%
3727.61 117
0.2%

GST
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size413.6 KiB
0.18
27343 
0.1
21314 
0.05
4145 
0.12
 
122

Length

Max length4
Median length4
Mean length3.5972716
Min length3

Characters and Unicode

Total characters190382
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.1
2nd row0.1
3rd row0.1
4th row0.18
5th row0.18

Common Values

ValueCountFrequency (%)
0.18 27343
51.7%
0.1 21314
40.3%
0.05 4145
 
7.8%
0.12 122
 
0.2%

Length

2024-02-26T08:00:04.622851image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-26T08:00:04.953964image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0.18 27343
51.7%
0.1 21314
40.3%
0.05 4145
 
7.8%
0.12 122
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 57069
30.0%
. 52924
27.8%
1 48779
25.6%
8 27343
14.4%
5 4145
 
2.2%
2 122
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 137458
72.2%
Other Punctuation 52924
 
27.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 57069
41.5%
1 48779
35.5%
8 27343
19.9%
5 4145
 
3.0%
2 122
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 52924
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 190382
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 57069
30.0%
. 52924
27.8%
1 48779
25.6%
8 27343
14.4%
5 4145
 
2.2%
2 122
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 190382
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 57069
30.0%
. 52924
27.8%
1 48779
25.6%
8 27343
14.4%
5 4145
 
2.2%
2 122
 
0.1%

쿠폰코드
Categorical

Distinct45
Distinct (%)0.1%
Missing400
Missing (%)0.8%
Memory size413.6 KiB
SALE20
6373 
SALE30
5915 
SALE10
5838 
ELEC10
4826 
ELEC30
4647 
Other values (40)
24925 

Length

Max length7
Median length6
Mean length5.8379217
Min length4

Characters and Unicode

Total characters306631
Distinct characters25
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowELEC10
2nd rowELEC10
3rd rowOFF10
4th rowSALE10
5th rowAIO10

Common Values

ValueCountFrequency (%)
SALE20 6373
12.0%
SALE30 5915
11.2%
SALE10 5838
11.0%
ELEC10 4826
9.1%
ELEC30 4647
8.8%
ELEC20 4540
 
8.6%
EXTRA10 2317
 
4.4%
OFF10 2250
 
4.3%
EXTRA20 2211
 
4.2%
OFF20 2202
 
4.2%
Other values (35) 11405
21.5%

Length

2024-02-26T08:00:05.089602image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sale20 6373
12.1%
sale30 5915
11.3%
sale10 5838
11.1%
elec10 4826
9.2%
elec30 4647
8.8%
elec20 4540
 
8.6%
extra10 2317
 
4.4%
off10 2250
 
4.3%
extra20 2211
 
4.2%
off20 2202
 
4.2%
Other values (35) 11405
21.7%

Most occurring characters

ValueCountFrequency (%)
E 56250
18.3%
0 52524
17.1%
L 32139
10.5%
A 27948
9.1%
S 18126
 
5.9%
2 17830
 
5.8%
1 17470
 
5.7%
3 17224
 
5.6%
C 14957
 
4.9%
F 13026
 
4.2%
Other values (15) 39137
12.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 201583
65.7%
Decimal Number 105048
34.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 56250
27.9%
L 32139
15.9%
A 27948
13.9%
S 18126
 
9.0%
C 14957
 
7.4%
F 13026
 
6.5%
O 8517
 
4.2%
R 7346
 
3.6%
T 6843
 
3.4%
X 6575
 
3.3%
Other values (11) 9856
 
4.9%
Decimal Number
ValueCountFrequency (%)
0 52524
50.0%
2 17830
 
17.0%
1 17470
 
16.6%
3 17224
 
16.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 201583
65.7%
Common 105048
34.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 56250
27.9%
L 32139
15.9%
A 27948
13.9%
S 18126
 
9.0%
C 14957
 
7.4%
F 13026
 
6.5%
O 8517
 
4.2%
R 7346
 
3.6%
T 6843
 
3.4%
X 6575
 
3.3%
Other values (11) 9856
 
4.9%
Common
ValueCountFrequency (%)
0 52524
50.0%
2 17830
 
17.0%
1 17470
 
16.6%
3 17224
 
16.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 306631
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 56250
18.3%
0 52524
17.1%
L 32139
10.5%
A 27948
9.1%
S 18126
 
5.9%
2 17830
 
5.8%
1 17470
 
5.7%
3 17224
 
5.6%
C 14957
 
4.9%
F 13026
 
4.2%
Other values (15) 39137
12.8%

할인율
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size413.6 KiB
20.0
17830 
10.0
17470 
30.0
17224 
0.0
 
400

Length

Max length4
Median length4
Mean length3.992442
Min length3

Characters and Unicode

Total characters211296
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10.0
2nd row10.0
3rd row10.0
4th row10.0
5th row10.0

Common Values

ValueCountFrequency (%)
20.0 17830
33.7%
10.0 17470
33.0%
30.0 17224
32.5%
0.0 400
 
0.8%

Length

2024-02-26T08:00:05.259147image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-26T08:00:05.378829image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
20.0 17830
33.7%
10.0 17470
33.0%
30.0 17224
32.5%
0.0 400
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 105848
50.1%
. 52924
25.0%
2 17830
 
8.4%
1 17470
 
8.3%
3 17224
 
8.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 158372
75.0%
Other Punctuation 52924
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 105848
66.8%
2 17830
 
11.3%
1 17470
 
11.0%
3 17224
 
10.9%
Other Punctuation
ValueCountFrequency (%)
. 52924
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 211296
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 105848
50.1%
. 52924
25.0%
2 17830
 
8.4%
1 17470
 
8.3%
3 17224
 
8.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 211296
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 105848
50.1%
. 52924
25.0%
2 17830
 
8.4%
1 17470
 
8.3%
3 17224
 
8.2%

Interactions

2024-02-26T07:59:57.205033image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-26T07:59:53.356491image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-26T07:59:54.146378image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-26T07:59:54.987961image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-26T07:59:55.695071image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-26T07:59:56.396197image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-26T07:59:57.347652image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-26T07:59:53.509082image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-26T07:59:54.266060image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-26T07:59:55.109636image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-26T07:59:55.802784image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-26T07:59:56.503909image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-26T07:59:57.576041image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-26T07:59:53.668657image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-26T07:59:54.396544image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-26T07:59:55.234303image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-26T07:59:55.945402image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-26T07:59:56.627579image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-26T07:59:57.883219image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-26T07:59:53.793323image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-26T07:59:54.519216image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-26T07:59:55.347999image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-26T07:59:56.059097image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-26T07:59:56.750249image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-26T07:59:58.031823image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-26T07:59:53.923973image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-26T07:59:54.652858image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-26T07:59:55.462692image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-26T07:59:56.173791image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-26T07:59:56.925779image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-26T07:59:58.140532image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-26T07:59:54.036674image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-26T07:59:54.848334image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-26T07:59:55.579380image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-26T07:59:56.283497image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-26T07:59:57.069396image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Missing values

2024-02-26T07:59:58.339998image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-26T07:59:58.832680image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

고객ID거래ID거래날짜제품ID제품카테고리수량평균금액배송료쿠폰상태성별고객지역가입기간오프라인비용온라인비용GST쿠폰코드할인율
0USER_1358Transaction_00002019-01-01Product_0981Nest-USA1153.716.5UsedChicago1245002424.50.10ELEC1010.0
1USER_1358Transaction_00012019-01-01Product_0981Nest-USA1153.716.5UsedChicago1245002424.50.10ELEC1010.0
2USER_1358Transaction_00022019-01-01Product_0904Office12.056.5UsedChicago1245002424.50.10OFF1010.0
3USER_1358Transaction_00032019-01-01Product_0203Apparel517.536.5Not UsedChicago1245002424.50.18SALE1010.0
4USER_1358Transaction_00032019-01-01Product_0848Bags116.506.5UsedChicago1245002424.50.18AIO1010.0
5USER_1358Transaction_00032019-01-01Product_0854Bags155.156.5UsedChicago1245002424.50.18AIO1010.0
6USER_1358Transaction_00032019-01-01Product_0880Drinkware153.086.5Not UsedChicago1245002424.50.18EXTRA1010.0
7USER_1358Transaction_00032019-01-01Product_0885Drinkware1510.316.5ClickedChicago1245002424.50.18EXTRA1010.0
8USER_1358Transaction_00032019-01-01Product_0898Drinkware59.276.5UsedChicago1245002424.50.18EXTRA1010.0
9USER_0190Transaction_00032019-01-01Product_0914Office520.986.5UsedCalifornia4345002424.50.10OFF1010.0
고객ID거래ID거래날짜제품ID제품카테고리수량평균금액배송료쿠폰상태성별고객지역가입기간오프라인비용온라인비용GST쿠폰코드할인율
52914USER_0504Transaction_250532019-12-31Product_0992Nest1100.916.50ClickedNew York4540002058.750.05NE3030.0
52915USER_0504Transaction_250542019-12-31Product_0976Nest-USA1121.306.50Not UsedNew York4540002058.750.10ELEC3030.0
52916USER_0504Transaction_250542019-12-31Product_0984Nest-USA280.526.50ClickedNew York4540002058.750.10ELEC3030.0
52917USER_0504Transaction_250542019-12-31Product_0992Nest1100.916.50ClickedNew York4540002058.750.05NE3030.0
52918USER_0504Transaction_250552019-12-31Product_0992Nest3100.916.50ClickedNew York4540002058.750.05NE3030.0
52919USER_0504Transaction_250562019-12-31Product_0976Nest-USA1121.306.50ClickedNew York4540002058.750.10ELEC3030.0
52920USER_0504Transaction_250572019-12-31Product_0413Apparel148.926.50UsedNew York4540002058.750.18SALE3030.0
52921USER_0504Transaction_250582019-12-31Product_0989Nest-USA1151.886.50UsedNew York4540002058.750.10ELEC3030.0
52922USER_0562Transaction_250592019-12-31Product_0985Nest-USA580.526.50ClickedCalifornia740002058.750.10ELEC3030.0
52923USER_0562Transaction_250602019-12-31Product_0984Nest-USA480.5219.99ClickedCalifornia740002058.750.10ELEC3030.0